A multi-stage classification system for detecting intrusions in computer networks

A serial multi-stage classification system for facing the problem of intrusion detection in computer networks is proposed. The whole decision process is organized into successive stages, each one using a set of features tailored for recognizing a specific attack category. All the stages employ suitable criteria for estimating the reliability of the performed classification, so that, in case of uncertainty, information related to a possible attack are only logged for further processing, without raising an alert for the system manager. This permits to reduce the number of false alarms. On the other hand, in order to keep low the number of missed detections, the proposed system declares a connection as normal traffic only if all the stages do not detect an attack. The proposed multi-stage intrusion detection system has been tested on three different services (http, telnet and ftp) of a standard database used for benchmarking intrusion detection systems and also on real network traffic data. The experimental analysis highlights the effectiveness of the approach: the proposed system behaves significantly better than other multiple classifier systems performing classification in a single stage.

Face recognition using an NNSRM classifier in LDA subspace

NNSRM is an implementation of the structural risk minimization (SRM) principle using the nearest neighbor (NN) rule, and linear discriminant analysis (LDA) is a dimension-reducing method, which is usually used in classifications. This paper combines the two methods for face recognition. We first project the face images into a PCA subspace, then project the results into a much lower-dimensional LDA subspace, and then use an NNSRM classifier to recognize them in the LDA subspace. Experimental results demonstrate that the combined method can achieve a better performance than NN by selecting different distances and a comparable performance with SVM but costing less computational time.

Reverse engineering the way humans rank textures

We argue that in order to understand which features are used by humans to group textures, one must start by computing thousands of features of diverse nature, and select from those features those that allow the reproduction of perceptual groups or perceptual ranking created by humans. We use the Trace transform to produce such features here. We compare these features with those produced from the co-occurrence matrix and its variations. We show that when one is not interested in reproducing human behaviour, the elements of the co-occurrence matrix used as features perform best in terms of texture classification accuracy. However, these features cannot be “trained” or “selected” to imitate human ranking, while the features produced from the Trace transform can. We attribute this to the diverse nature of the features computed from the Trace transform.

Quadtree-based eigendecomposition for pose estimation in the presence of occlusion and background clutter

Eigendecomposition-based techniques are popular for a number of computer vision problems, e.g., object and pose estimation, because they are purely appearance based and they require few on-line computations. Unfortunately, they also typically require an unobstructed view of the object whose pose is being detected. The presence of occlusion and background clutter precludes the use of the normalizations that are typically applied and significantly alters the appearance of the object under detection. This work presents an algorithm that is based on applying eigendecomposition to a quadtree representation of the image dataset used to describe the appearance of an object. This allows decisions concerning the pose of an object to be based on only those portions of the image in which the algorithm has determined that the object is not occluded. The accuracy and computational efficiency of the proposed approach is evaluated on 16 different objects with up to 50% of the object being occluded and on images of ships in a dockyard.

Breadth-first search strategies for trie-based syntactic pattern recognition

Dictionary-based syntactic pattern recognition of strings attempts to recognize a transmitted string X ^*, by processing its noisy version, Y, without sequentially comparing Y with every element X in the finite, (but possibly, large) dictionary, H. The best estimate X ⁺ of X ^*, is defined as that element of H which minimizes the generalized Levenshtein distance (GLD) D(X, Y) between X and Y, for all X ∈H. The non-sequential PR computation of X ⁺ involves a compact trie-based representation of H. In this paper, we show how we can optimize this computation by incorporating breadth first search schemes on the underlying graph structure. This heuristic emerges from the trie-based dynamic programming recursive equations, which can be effectively implemented using a new data structure called the linked list of prefixes that can be built separately or “on top of” the trie representation of H. The new scheme does not restrict the number of errors in Y to be merely a small constant, as is done in most of the available methods. The main contribution is that our new approach can be used for generalized GLDs and not merely for 0/1 costs. It is also applicable when all possible correct candidates need to be known, and not just the best match. These constitute the cases when the “cutoffs” cannot be used in the DFS trie-based technique (Shang and Merrettal in IEEE Trans Knowl Data Eng 8(4):540–547, 1996). The new technique is compared with the DFS trie-based technique (Risvik in United Patent 6377945 B1, 23 April 2002; Shang and Merrettal in IEEE Trans Knowl Data Eng 8(4):540–547, 1996) using three large and small benchmark dictionaries with different errors. In each case, we demonstrate marked improvements with regard to the operations needed up to 21%, while at the same time maintaining the same accuracy. Additionally, some further improvements can be obtained by introducing the knowledge of the maximum number or percentage of errors in Y.

Periodicity and stability issues of a <Emphasis Type="Italic">chaotic</Emphasis> pattern recognition neural network

Traditional pattern recognition (PR) systems work with the model that the object to be recognized is characterized by a set of features, which are treated as the inputs. In this paper, we propose a new model for PR, namely one that involves chaotic neural networks (CNNs). To achieve this, we enhance the basic model proposed by Adachi (Neural Netw 10:83–98, 1997), referred to as Adachi’s Neural Network (AdNN), which though dynamic, is not chaotic. We demonstrate that by decreasing the multiplicity of the eigenvalues of the AdNN’s control system, we can effectively drive the system into chaos. We prove this result here by eigenvalue computations and the evaluation of the Lyapunov exponent. With this premise, we then show that such a Modified AdNN (M-AdNN) has the desirable property that it recognizes various input patterns. The way that this PR is achieved is by the system essentially sympathetically “resonating” with a finite periodicity whenever these samples (or their reasonable resemblances) are presented. In this paper, we analyze the M-AdNN for its periodicity, stability and the length of the transient phase of the retrieval process. The M-AdNN has been tested for Adachi’s dataset and for a real-life PR problem involving numerals. We believe that this research also opens a host of new research avenues. Research partially supported by the Natural Sciences and Engineering Research Council of Canada.

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Estimation of distributions involving unobservable events: the case of optimal search with unknown Target Distributions

We consider the problem of estimating the parameters of a distribution when the underlying events are themselves unobservable. The aim of the exercise is to perform a task (for example, search a web-site or query a distributed database) based on a distribution involving the state of nature, except that we are not allowed to observe the various “states of nature” involved in this phenomenon. In particular, we concentrate on the task of searching for an object in a set of N locations (or bins) {C ₁, C ₂,…, C _N }, in which the probability of the object being in the location C _i is p _i , where P = [p ₁, p ₂,…, p _N ] ^T is called the Target Distribution. Also, the probability of locating the object in the bin within a specified time, given that it is in the bin, is given by a function called the Detection function, which, in its most common instantiation, is typically, specified by an exponential function. The intention is to allocate the available resources so as to maximize the probability of locating the object. The handicap, however, is that the time allowed is limited, and thus the fact that the object is not located in bin C _i within a specified time does not necessarily imply that the object is not in C _i . This problem has applications in searching large databases, distributed databases, and the world-wide web, where the location of the files sought for are unknown, and in developing various military and strategic policies. All of the research done in this area has assumed the knowledge of the {p _i }. In this paper we consider the problem of obtaining error bounds, estimating the Target Distribution, and allocating the search times when the {p _i } are unknown. To the best of our knowledge, these results are of a pioneering sort - they are the first available results in this area, and are particularly interesting because, as mentioned earlier, the events concerning the Target Distribution, in themselves, are unobservable.

Semi-supervised discriminative classification with application to tumorous tissues segmentation of MR brain images

Due to the large data size of 3D MR brain images and the blurry boundary of the pathological tissues, tumor segmentation work is difficult. This paper introduces a discriminative classification algorithm for semi-automated segmentation of brain tumorous tissues. The classifier uses interactive hints to obtain models to classify normal and tumor tissues. A non-parametric Bayesian Gaussian random field in the semi-supervised mode is implemented. Our approach uses both labeled data and a subset of unlabeled data sampling from 2D/3D images for training the model. Fast algorithm is also developed. Experiments show that our approach produces satisfactory segmentation results comparing to the manually labeled results by experts.

Multi-level classification of emphysema in HRCT lung images

Emphysema is a common chronic respiratory disorder characterised by the destruction of lung tissue. It is a progressive disease where the early stages are characterised by a diffuse appearance of small air spaces, and later stages exhibit large air spaces called bullae. A bullous region is a sharply demarcated region of emphysema. In this paper, it is shown that an automated texture-based system based on co-training is capable of achieving multiple levels of emphysema extraction in high-resolution computed tomography (HRCT) images. Co-training is a semi-supervised technique used to improve classifiers that are trained with very few labelled examples using a large pool of unseen examples over two disjoint feature sets called views. It is also shown that examples labelled by experts can be incorporated within the system in an incremental manner. The results are also compared against “density mask”, currently a standard approach used for emphysema detection in medical image analysis and other computerized techniques used for classification of emphysema in the literature. The new system can classify diffuse regions of emphysema starting from a bullous setting. The classifiers built at different iterations also appear to show an interesting correlation with different levels of emphysema, which deserves more exploration.

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Human action recognition in videos based on the Transferable Belief Model

This paper focuses on human behavior recognition where the main problem is to bridge the semantic gap between the analogue observations of the real world and the symbolic world of human interpretation. For that, a fusion architecture based on the Transferable Belief Model framework is proposed and applied to action recognition of an athlete in video sequences of athletics meeting with moving camera. Relevant features are extracted from videos, based on both the camera motion analysis and the tracking of particular points on the athlete’s silhouette. Some models of interpretation are used to link the numerical features to the symbols to be recognized, which are running, jumping and falling actions. A Temporal Belief Filter is then used to improve the robustness of action recognition. The proposed approach demonstrates good performance when tested on real videos of athletics sports videos (high jumps, pole vaults, triple jumps and long jumps) acquired by a moving camera and different view angles. The proposed system is also compared to Bayesian Networks.

Analyzing the efficacy of using digital ink devices in a learning environment

There has been increased interest on the impact of mobile devices such as PDAs and Tablet PCs in introducing new pedagogical approaches and active learning experiences. We propose an intelligent system that efficiently addresses the inherent subjectivity in student perception of note taking and information retrieval. We employ the idea of cross indexing the digital ink notes with matching electronic documents in the repository. Latent Semantic Indexing is used to perform document and page level indexing. Thus for each retrieved document, the user can go over to the relevant pages that match the query. Techniques to handle problems such as polysemy (multiple meanings of a word) in large databases, document folding and no match for query are discussed. We tested our system for its performance, usability and effectiveness in the learning process. The results from the exploratory studies reveal that the proposed system provides a highly enhanced student learning experience, thereby facilitating high test scores.

The aspect Bernoulli model: multiple causes of presences and absences

We present a probabilistic multiple cause model for the analysis of binary (0–1) data. A distinctive feature of the aspect Bernoulli (AB) model is its ability to automatically detect and distinguish between “true absences” and “false absences” (both of which are coded as 0 in the data), and similarly, between “true presences” and “false presences” (both of which are coded as 1). This is accomplished by specific additive noise components which explicitly account for such non-content bearing causes. The AB model is thus suitable for noise removal and data explanatory purposes, including omission/addition detection. An important application of AB that we demonstrate is data-driven reasoning about palaeontological recordings. Additionally, results on recovering corrupted handwritten digit images and expanding short text documents are also given, and comparisons to other methods are demonstrated and discussed.

Wiener estimation method in estimating of spectral reflectance from RGB images

Color is one of the most important features in digital images. The representation of color in digital form with a three-component image (RGB) is not very accurate, hence the use of a multiple-component spectral image is justified. At the moment, acquiring a spectral image is not as easy and as fast as acquiring a conventional three-component image. One answer to this problem is to use a regular digital RGB camera and estimate its RGB image into a spectral image by the Wiener estimation method, which is based on the use of a priori knowledge. In this paper, the Wiener estimation method is used to estimate the spectra of icons. The experimental results of the spectral estimation are presented. The text was submitted by the authors in English. Pekka Tapani Stigell. Year of birth 1976. Year of graduation and name of institution: Last year undergraduate student in the Department of Computer Science in the University of Joensuu, Finland. Affiliation: InFotoics Center, Department of Computer Science, University of Joensuu. Position: Trainee. Area of research: Color research. Number of publications: 1. Membership to scientific societies: Pattern Recognition Society of Finland, member-society of IAPR (International Association for Pattern Recognition). Prizes for achievements in research or applications: The best young scientist award in PRIA-7-2004 (shared with two other scientists). Kimiyoshi Miyata. Year of birth: 1966. Year of graduation and name of institution: 2000. Graduate School of Science and Technology, Chiba University, Japan. Year of graduation: 1990, BE degree (Chiba University), 1992, ME degree (Chiba University), 2000, Ph.D degree (Chiba University). Affiliation: Museum Science Division, Research Department, National Museum of Japanese History. Position: Assistant Professor. Area of research: Improvement of image quality, color management, application of imaging science and technology to museum activities. Number of publications: 11. Membership to scientific societies: Society of Photographic Science and Technology of Japan, Optical Society of Japan, Institute of Image Electronics Engineers of Japan, Society for Imaging Science and Technology. Prizes for achievements in research or applications: Progressing Award from Society of Photographic Science and Technology of Japan in 2000, Itek Award from Society for Imaging Science and Technology in 2000. Markku Hauta-Kasari. Year of birth: 1970. Graduation and name of the institution: University of Technology, Lappeenranta, Finland. Year of graduation: 1999, Ph.D. degree (University of Technology, Lappeenranta). Affiliation: InFotonics Center, Department of Computer Science, University of Joensuu. Position: Director. Area of research: Color research, neural computation, pattern recognition, optical pattern recognition, computer vision, image processing. Number of publications: Articles in refereed international scientific journals: 5, Articles in refereed international scientific conferences: 9, Other Scientific Publications: 40. Membership to academies: Chairman of the Pattern Recognition Society of Finland May 2003. Membership to scientific societies: Pattern Recognition Society of Finland, member-society of IAPR (International Association for Pattern Recognition), Finnish Information Processing Association, Finnish Union of University Researchers and Teachers, Optical Society of Japan, Optical Society of America. Prizes for achievements in research or applications: The best Ph.D.-thesis award in the field of pattern recognition in 1998–1999 in Finland. Award was issued by the Pattern Recognition Society of Finland on April 25, 2000.     

FRCT: fuzzy-rough classification trees

Using fuzzy-rough hybrids, we have proposed a measure to quantify the functional dependency of decision attribute(s) on condition attribute(s) within fuzzy data. We have shown that the proposed measure of dependency degree is a generalization of the measure proposed by Pawlak for crisp data. In this paper, this new measure of dependency degree has been encapsulated into the decision tree generation mechanism to produce fuzzy-rough classification trees (FRCT); efficient, top-down, multi-class decision tree structures geared to solving classification problems from feature-based learning examples. The developed FRCT generation algorithm has been applied to 16 real-world benchmark datasets. It is experimentally compared with the five fuzzy decision tree generation algorithms reported so far, and the rough decomposition tree algorithm. Comparison has been made in terms of number of rules, average training time, and classification accuracy. Experimental results show that the proposed algorithm to generate FRCT outperforms existing fuzzy decision tree generation techniques and rough decomposition tree induction algorithm.

Advances in GML for Geospatial Applications

This paper presents a study of Geography Markup Language (GML), the issues that arise from using GML for spatial applications, including storage, parsing, querying and visualization, as well as the use of GML for mobile devices and web services. GML is a modeling language developed by the Open Geospatial Consortium (OGC) as a medium of uniform geographic data storage and exchange among diverse applications. Many new XML-based languages are being developed as open standards in various areas of application. It would be beneficial to integrate such languages with GML during the developmental stages, taking full advantage of a non-proprietary universal standard. As GML is a relatively new language still in development, data processing techniques need to be refined further in order for GML to become a more efficient medium for geospatial applications.

Distributional-based texture classification using non-parametric statistics

Texture classification is an important problem in image analysis. In the present study, an efficient strategy for classifying texture images is introduced and examined within a distributional-statistical framework. Our approach incorporates the multivariate Wald–Wolfowitz test (WW-test), a non-parametric statistical test that measures the similarity between two different sets of multivariate data, which is utilized here for comparing texture distributions. By summarizing the texture information using standard feature extraction methodologies, the similarity measure provides a comprehensive estimate of the match between different images based on graph theory. The proposed “distributional metric” is shown to handle efficiently the texture-space dimensionality and the limited sample size drawn from a given image. The experimental results, from the application on a typical texture database, clearly demonstrate the effectiveness of our approach and its superiority over other well-established texture distribution (dis)similarity metrics. In addition, its performance is used to evaluate several approaches for texture representation. Even though the classification results are obtained on grayscale images, a direct extension to color-based ones can be straightforward.

Improving the space cost of <Emphasis Type="Italic">k</Emphasis>-NN search in metric spaces by using distance estimators

Similarity searching in metric spaces has a vast number of applications in several fields like multimedia databases, text retrieval, computational biology, and pattern recognition. In this context, one of the most important similarity queries is the k nearest neighbor (k-NN) search. The standard best-first k-NN algorithm uses a lower bound on the distance to prune objects during the search. Although optimal in several aspects, the disadvantage of this method is that its space requirements for the priority queue that stores unprocessed clusters can be linear in the database size. Most of the optimizations used in spatial access methods (for example, pruning using MinMaxDist) cannot be applied in metric spaces, due to the lack of geometric properties. We propose a new k-NN algorithm that uses distance estimators, aiming to reduce the storage requirements of the search algorithm. The method stays optimal, yet it can significantly prune the priority queue without altering the output of the query. Experimental results with synthetic and real datasets confirm the reduction in storage space of our proposed algorithm, showing savings of up to 80% of the original space requirement.

A practical approach to testing GUI systems

GUI systems are becoming increasingly popular thanks to their ease of use when compared against traditional systems. However, GUI systems are often challenging to test due to their complexity and special features. Traditional testing methodologies are not designed to deal with the complexity of GUI systems; using these methodologies can result in increased time and expense. In our proposed strategy, a GUI system will be divided into two abstract tiers—the component tier and the system tier. On the component tier, a flow graph will be created for each GUI component. Each flow graph represents a set of relationships between the pre-conditions, event sequences and post-conditions for the corresponding component. On the system tier, the components are integrated to build up a viewpoint of the entire system. Tests on the system tier will interrogate the interactions between the components. This method for GUI testing is simple and practical; we will show the effectiveness of this approach by performing two empirical experiments and describing the results found.

Optimizing two-pass connected-component labeling algorithms

We present two optimization strategies to improve connected-component labeling algorithms. Taking together, they form an efficient two-pass labeling algorithm that is fast and theoretically optimal. The first optimization strategy reduces the number of neighboring pixels accessed through the use of a decision tree, and the second one streamlines the union-find algorithms used to track equivalent labels. We show that the first strategy reduces the average number of neighbors accessed by a factor of about 2. We prove our streamlined union-find algorithms have the same theoretical optimality as the more sophisticated ones in literature. This result generalizes an earlier one on using union-find in labeling algorithms by Fiorio and Gustedt (Theor Comput Sci 154(2):165–181, 1996). In tests, the new union-find algorithms improve a labeling algorithm by a factor of 4 or more. Through analyses and experiments, we demonstrate that our new two-pass labeling algorithm scales linearly with the number of pixels in the image, which is optimal in computational complexity theory. Furthermore, the new labeling algorithm outperforms the published labeling algorithms irrespective of test platforms. In comparing with the fastest known labeling algorithm for two-dimensional (2D) binary images called contour tracing algorithm, our new labeling algorithm is up to ten times faster than the contour tracing program distributed by the original authors.

Empirical evaluation of optimization algorithms when used in goal-oriented automated test data generation techniques

Software testing is an essential process in software development. Software testing is very costly, often consuming half the financial resources assigned to a project. The most laborious part of software testing is the generation of test-data. Currently, this process is principally a manual process. Hence, the automation of test-data generation can significantly cut the total cost of software testing and the software development cycle in general. A number of automated test-data generation approaches have already been explored. This paper highlights the goal-oriented approach as a promising approach to devise automated test-data generators. A range of optimization techniques can be used within these goal-oriented test-data generators, and their respective characteristics, when applied to these situations remain relatively unexplored. Therefore, in this paper, a comparative study about the effectiveness of the most commonly used optimization techniques is conducted.